Dynamic Viscosity Analysis of Fuels and Their Blends with Bio-Additives as a Function of Temperature

The evolving landscape of the liquid fuels market, together with changing legal regulations, has prompted consideration of using artificial intelligence methods for the physicochemical analysis of fuel and biofuel blends. The objective of the study was to determine the dynamic viscosity of diesel fuel and its blends with vegetable oils derived from rapeseed, camelina, flax, and mustard. These oils were selected due to their previous applications in the petrochemical industry. The oils used in the study were obtained by cold pressing with a screw press. The measurements were performed over a temperature range of 5 to 85 °C at mass ratios containing 20%, 40%, 60%, and 80% vegetable oil in diesel fuel. A Brookfield-type rotational viscometer was employed for the measurements. Based on the resulting laboratory data, mathematical models of dynamic viscosity were developed. Furthermore, the experimental results were used to train a neural network to analyse relationships among dynamic viscosity, temperature, and vegetable oil content in the blend. Both the empirical (mathematical) models and the models describing changes in dynamic viscosity as a function of temperature and component content in the vegetable oil–diesel blends achieved coefficients of determination (R2) exceeding 0.99.